Province of Origin, Decision-Making Bias, and Responses to Bureaucratic Versus Algorithmic Decision-Making

• 成果类型: Article; Early Access
• 署名作者: Wang, Ge; Zhang, Zhejun; Xie, Shenghua; Guo, Yue
• 署名单位: Central China Normal University; Beijing Normal University
• 刊物名称: PUBLIC ADMINISTRATION REVIEW
• ISSN/ISSBN: 0033-3352
• DOI: 10.1111/puar.13928
• 发表日期: 2025
• 关键词: representative bureaucracy; artificial-intelligence; street-level; administrative discretion; RACE; governance; JUSTICE; GENDER; POLICY; region

摘要

As algorithmic decision-making (ADM) becomes prevalent in certain public sectors, its interaction with traditional bureaucratic decision-making (BDM) evolves, especially in contexts shaped by regional identities and decision-making biases. To explore these dynamics, we conducted two survey experiments within traffic enforcement scenarios, involving 4816 participants across multiple provinces. Results indicate that non-native residents perceived ADM as fairer and more acceptable than BDM when they did not share a province of origin with local bureaucrats. Both native and non-native residents showed a preference for ADM in the presence of bureaucratic and algorithmic biases but preferred BDM when such biases were absent. When bureaucratic and algorithmic biases coexisted, the lack of a shared province of origin further reinforced non-native residents' perception of ADM as fairer and more acceptable than BDM. Our findings reveal the complex interplay among province of origin, decision-making biases, and responses to different decision-making approaches.